9,239 research outputs found
Stochastic Dynamics of Cascading Failures in Electric-Cyber Infrastructures
Emerging smart grids consist of tightly-coupled systems, namely a power grid and a communication system. While today\u27s power grids are highly reliable and modern control and communication systems have been deployed to further enhance their reliability, historical data suggest that they are yet vulnerable to large failures. A small set of initial disturbances in power grids in conjunction with lack of effective, corrective actions in a timely manner can trigger a sequence of dependent component failures, called cascading failures. The main thrust of this dissertation is to build a probabilistic framework for modeling cascading failures in power grids while capturing their interactions with the coupled communication systems so that the risk of cascading failures in the composite complex electric-cyber infrastructures can be examined, analyzed and predicted. A scalable and analytically tractable continuous-time Markov chain model for stochastic dynamics of cascading failures in power grids is constructed while retaining key physical attributes and operating characteristics of the power grid. The key idea of the proposed framework is to simplify the state space of the complex power system while capturing the effects of the omitted variables through the transition probabilities and their parametric dependence on physical attributes and operating characteristics of the system. In particular, the effects of the interdependencies between the power grid and the communication system have been captured by a parametric formulation of the transition probabilities using Monte-Carlo simulations of cascading failures. The cascading failures are simulated with a coupled power-system simulation framework, which is also developed in this dissertation. Specifically, the probabilistic model enables the prediction of the evolution of the blackout probability in time. Furthermore, the asymptotic analysis of the blackout probability as time tends to infinity enables the calculation of the probability mass function of the blackout size, which has been shown to have a heavy tail, e.g., power-law distribution, specifically when the grid is operating under stress scenarios. A key benefit of the model is that it enables the characterization of the severity of cascading failures in terms of a set of operating characteristics of the power grid. As a generalization to the Markov chain model, a regeneration-based model for cascading failures is also developed. The regeneration-based framework is capable of modeling cascading failures in a more general setting where the probability distribution of events in the system follows an arbitrarily specified distribution with non-Markovian characteristics. Further, a novel interdependent Markov chain model is developed, which provides a general probabilistic framework for capturing the effects of interactions among interdependent infrastructures on cascading failures. A key insight obtained from this model is that interdependencies between two systems can make two individually reliable systems behave unreliably. In particular, we show that due to the interdependencies two chains with non-heavy tail asymptotic failure distribution can result in a heavy tail distribution when coupled. Lastly, another aspect of future smart grids is studied by characterizing the fundamental bounds on the information rate in the sensor network that monitors the power grid. Specifically, a distributed source coding framework is presented that enables an improved estimate of the lower bound for the minimum required communication capacity to accurately describe the state of components in the information-centric power grid. The models developed in this dissertation provide critical understanding of cascading failures in electric-cyber infrastructures and facilitate reliable and quick detection of the risk of blackouts and precursors to cascading failures. These capabilities can guide the design of efficient communication systems and cascade aware control policies for future smart grids
Stochastic Model for Power Grid Dynamics
We introduce a stochastic model that describes the quasi-static dynamics of
an electric transmission network under perturbations introduced by random load
fluctuations, random removing of system components from service, random repair
times for the failed components, and random response times to implement optimal
system corrections for removing line overloads in a damaged or stressed
transmission network. We use a linear approximation to the network flow
equations and apply linear programming techniques that optimize the dispatching
of generators and loads in order to eliminate the network overloads associated
with a damaged system. We also provide a simple model for the operator's
response to various contingency events that is not always optimal due to either
failure of the state estimation system or due to the incorrect subjective
assessment of the severity associated with these events. This further allows us
to use a game theoretic framework for casting the optimization of the
operator's response into the choice of the optimal strategy which minimizes the
operating cost. We use a simple strategy space which is the degree of tolerance
to line overloads and which is an automatic control (optimization) parameter
that can be adjusted to trade off automatic load shed without propagating
cascades versus reduced load shed and an increased risk of propagating
cascades. The tolerance parameter is chosen to describes a smooth transition
from a risk averse to a risk taken strategy...Comment: framework for a system-level analysis of the power grid from the
viewpoint of complex network
Modelling interdependencies between the electricity and information infrastructures
The aim of this paper is to provide qualitative models characterizing
interdependencies related failures of two critical infrastructures: the
electricity infrastructure and the associated information infrastructure. The
interdependencies of these two infrastructures are increasing due to a growing
connection of the power grid networks to the global information infrastructure,
as a consequence of market deregulation and opening. These interdependencies
increase the risk of failures. We focus on cascading, escalating and
common-cause failures, which correspond to the main causes of failures due to
interdependencies. We address failures in the electricity infrastructure, in
combination with accidental failures in the information infrastructure, then we
show briefly how malicious attacks in the information infrastructure can be
addressed
Model reduction for analysis of cascading failures in power systems
In this paper, we apply a principal-orthogonal decomposition based method to the model reduction of a hybrid, nonlinear model of a power network. The results demonstrate that the sequence of fault events can be evaluated and predicted without necessarily simulating the whole system
Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology
In a cascading power transmission outage, component outages propagate
non-locally, after one component outages, the next failure may be very distant,
both topologically and geographically. As a result, simple models of
topological contagion do not accurately represent the propagation of cascades
in power systems. However, cascading power outages do follow patterns, some of
which are useful in understanding and reducing blackout risk. This paper
describes a method by which the data from many cascading failure simulations
can be transformed into a graph-based model of influences that provides
actionable information about the many ways that cascades propagate in a
particular system. The resulting "influence graph" model is Markovian, in that
component outage probabilities depend only on the outages that occurred in the
prior generation. To validate the model we compare the distribution of cascade
sizes resulting from contingencies in a branch test case to
cascade sizes in the influence graph. The two distributions are remarkably
similar. In addition, we derive an equation with which one can quickly identify
modifications to the proposed system that will substantially reduce cascade
propagation. With this equation one can quickly identify critical components
that can be improved to substantially reduce the risk of large cascading
blackouts.Comment: Accepted for publication at the IEEE Transactions on Power System
Depth penetration and scope extension of failures in the cascading of multilayer networks
Real-world complex systems always interact with each other, which causes
these systems to collapse in an avalanche or cascading manner in the case of
random failures or malicious attacks. The robustness of multilayer networks has
attracted great interest, where the modeling and theoretical studies of which
always rely on the concept of multilayer networks and percolation methods. A
straightforward and tacit assumption is that the interdependence across network
layers is strong, which means that a node will fail entirely with the removal
of all links if one of its interdependent neighbours fails. However, this
oversimplification cannot describe the general form of interactions across the
network layers in a real-world multilayer system. In this paper, we reveal the
nature of the avalanche disintegration of general multilayer networks with
arbitrary interdependency strength across network layers. Specifically, we
identify that the avalanche process of the whole system can essentially be
decomposed into two microscopic cascading dynamics in terms of the propagation
direction of the failures: depth penetration and scope extension. In the
process of depth penetration, the failures propagate from layer to layer, where
the greater the number of failed nodes is, the greater the destructive power
that will emerge in an interdependency group. In the process of scope
extension, failures propagate with the removal of connections in each network
layer. Under the synergy of the two processes, we find that the percolation
transition of the system can be discontinuous or continuous with changes in the
interdependency strength across network layers, which means that sudden
system-wide collapse can be avoided by controlling the interdependency strength
across network layers
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